Gesture-control systems provide simple and intuitional convenient operation. However, the systems using touch-controlled human-machine interfaces such as touch panels require users to perform operation by directly contacting the interfaces, thus being inconvenient to some applications. Contrary, the gesture-control systems using non-touch interfaces allows users to perform operation at a relatively distant place therefrom, while being more difficult to implement because such systems determine gestures by capturing and identifying images.
A well-known method for dynamic gesture identification includes receiving visible-light images, from which regions of skin color are recognized, identifying the shapes of the regions of skin color to find out the positions of a hand, and tracing the hand's displacements by detecting the variation of the hand's positions in successive images to identify the operation gesture. However, since skin-color analysis needs complex algorithm, and is highly dependent on the ambient light, it suffers from a higher error rate. Identification of the shape of a gesture also needs complex algorithm, and requires users to maintain a certain gesture shape, for example, five fingers fully separated or a V-sign made with fingers. Thus, this method is unable to identify some operation gestures, for example, overturning a palm, opening a fist into an open palm, and making a fist from an open palm. Structural diverseness of users' hands also increases difficulty in identification or the error rate. Other disadvantages include impossible identification performed in a darker environment, and requirement of a particular starting palm pose. Due to the dependence of shape identification for tracing the displacements of a hand, such methods are effective for only the operation gestures moving in the X-axis or Y-axis of the images, while unable to identify the operation gestures moving in the Z-axis of the images, for example, a hand moving forward or backward. In some applications, for example, for mobile phones and notebook computers, where there may be objects moving at the back of the user, the resultant identification may be interfered and, in turn, misled.
In another well-known method for gesture identification based on successive images, for example, Microsoft's motion control system, Kinect, in addition to two-dimensional image analysis, a human skeleton model is further built up using the depth information of the images, and serves as a basis for tracing the variation of the hand's position to achieve gesture identification. This method requires even more complex algorithm and longer time for computing. While being applicable for detecting operation gestures moving in the Z-axis of the images, it is only effective at a constant operation distance, and gives merely a small range for users to move the gestures forward and back. In the event that the gestures are not made at the predetermined operation distance, or in the event that there is no sufficient space for the predetermined operation distance, such gesture identification systems can not be used. The rigidity in terms of operation distance gives a challenge to manufacturers of gesture identification systems. The manufacturers can only assume a reasonable operation distance, and use this assumed operation distance as a basis to design all the parameters for gesture identification. This greatly limits applications of the resultant identification systems.
The above-mentioned methods need a large number of computing operations, and thus require higher costs in both hardware and software, being not economic for some simpler applications. Also, the demanding computing operations can slow down the system response. In addition, the above-mentioned methods are not applicable to applications where only a short operation distance is given. For example, for the operation distance within 1 meter, the systems are less stable. Moreover, since the above-mentioned methods need skin-color and profile identification of the user's hand, they are not suitable for hands wearing gloves, hands with curled fingers, hands with defective fingers, and of course, objects other than human hands, for example, pens and paper rolls.